RIGHT TURN ON RED (RTOR)--ENCOURAGING RESULTS FOR THE GERMAN SOLUTION (ABSTRACT ONLY)
Bibliographic record
Abstract
Right Turn On Red (RTOR) is standard practice in the USA and Canada; until the 70s there were, however, two different solutions with differing levels of safety. RTOR has not met with approval from many sides in Europe; here it is generally not permitted to turn right on red. Subsequent to German re-unification, the green arrow contained in the GDRs Road Traffic Regulations came into force in the whole of Germany, initially on a trial basis. The green arrow permits RTOR; contrary to the practice in the USA, however, drivers must stop before turning right and of course respect pedestrians and traffic with the right-of-way. RTOR with a green arrow is common in eastern Germany but is still rare in western Germany. Since 1991 BAST carried out a series of research projects on the subject of RTOR when the green arrow is showing. The focal points of these projects constituted investigations into traffic safety (conflicts and accidents). However, they also dealt with issues connected with traffic flow and operation. The results are encouraging: the advantages relating to efficiency and emissions do not seem to be offset by disadvantages in safety. A panel of German experts is to complete a report by the summer of 1999 which will also concern recommendations for the future use of the green arrow. A considerable increase in the use of RTOR in Germany would trigger an international discussion in Europe and could be a starting point.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.046 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".